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Record W4205277887 · doi:10.1002/app.52105

Role of blend phase ratio in controlling morphology, compressive, and recovery behavior of low‐density polyethylene/ethylene‐vinyl acetate foams

2021· article· en· W4205277887 on OpenAlexaff
Pouya Katbab, Seyed Hassan Jafari, Ali Asghar Katbab, Marianna Kontopoulou

Bibliographic record

VenueJournal of Applied Polymer Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsQueen's University
Fundersnot available
KeywordsLow-density polyethyleneMaterials scienceCrystallinityComposite materialEthylene-vinyl acetatePolyethyleneViscoelasticityCompression moldingCompressive strengthCopolymerPolymer

Abstract

fetched live from OpenAlex

Abstract This work presents a detailed study on the influence of blend phase ratio upon the morphology, crystallinity, mechanical and viscoelastic properties of cross‐linked foams based on blends of low‐density polyethylene (LDPE) and ethylene‐vinyl acetate copolymer (EVA). Non‐isothermal foaming is carried out via melt compression molding using dicumyl peroxide and azodicarbonamide as crosslinking and foaming agents, respectively. Rheo‐mechanical spectrometer characterization reveals faster crosslinking for EVA, leading to higher rate of melt viscosity increase for EVA‐rich blends during heating. This results in the formation of foams with higher cell density and gel content than LDPE‐rich foams. Compressive stress–strain behavior and viscoelastic hysteresis of the LDPE‐rich foams are governed by the degree of crystallinity and cell density, whereas for the EVA‐rich foams cell density and gel content are dominant. Increasing the crosslink density results in enhanced compressive behavior with excellent recovery of stress to 60% strain during four compression cycles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.252
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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